Mastering LLM Fine-Tuning and Model Merging: A Comprehensive Guide by Maxim Laon

Join Maxim Laon, Staff Machine Learning Scientist at Liquid AI, as he delves into the world of fine-tuning LLMs and model merging, sharing insights and best practices for unlocking the full potential of language models.

* The speaker is Maxim Laon, a Staff Machine Learning Scientist at Liquid AI, go developer expert, and author of "Hands-On Graph Neural Networks using Python."
* They will discuss fine-tuning language models (LLMs) and model merging.
* Fine-tuning involves three stages: pre-training, supervised fine-tuning, and preference alignment.
* Pre-training teaches the model to predict the next token in a text sample.
* Supervised fine-tuning uses question-answer pairs to teach the model to answer questions and follow instructions.
* Preference alignment further customizes the model's behavior based on human preferences.
* Fine-tuning is recommended when prompt engineering isn't enough, or when there's a need for control and customizability in enterprise settings.
* Libraries for fine-tuning include TRL from Hugging Face, AXEL, and lamb Factory, each with unique features and interfaces.
* Fine-tuning examples involve system prompts (e.g., "Answer like a 5-year-old") and user prompts (e.g., "Remove the spaces from this sentence").
* Synthetic data sets are often used in fine-tuning, generated by frontier models for higher quality.
* Preference alignment methods include Direct Preference Optimization (DPO), where the model is trained to output higher probabilities for chosen answers over rejected ones.
* When creating synthetic data sets, focus on accuracy, diversity, and complexity:
+ Accuracy: Factual correctness, avoiding fake information.
+ Diversity: Covering various topics and writing styles.
+ Complexity: Providing challenging tasks that force reasoning and deep understanding.
* For fine-tuning language models, consider hyperparameters such as the learning rate, number of epochs, sequence length, batch size, and lowers with rank.
* Model merging involves combining weights from different fine-tuned models to leverage community contributions effectively.
* Merged models can be created using techniques like SLURP (Spherical Linear Interpolation), where interpolation factors are tweaked for different layers.
* Techniques like pruning, AG that it has, and Frankenstein models can also be used in merging to reduce redundancy and combine multiple models.
* Model blending techniques include Pass Through, which concatenates layers from different LLMs, and Mixture of Experts, where a router selects the appropriate FFN layer for each token and each layer
* Merged models can outperform base models on various tasks.

Source: AI Engineer via YouTube

❓ What do you think? What is one key aspect of fine-tuning LLMs that Maxim Laon failed to mention, yet remains crucial for achieving successful model performance? Feel free to share your thoughts in the comments!